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Advancing vanadium redox flow battery analysis: a deep learning approach for high-throughput 3D visualization and bubble quantification

; ; ; ; ;

In
Digital discovery 4(10), Seiten/Artikel-Nr.:2724-2736

ImpressumCambridge ; London ; Washington DC : Royal Society of Chemistry

ISSN2635-098X

Online
DOI: 10.1039/D5DD00158G

DOI: 10.18154/RWTH-2026-03846
URL: https://publications.rwth-aachen.de/record/1032869/files/1032869.pdf

Einrichtungen

  1. Lehrstuhl für Theorie und computergestützte Modellierung von Energiematerialien (526810)
  2. Fachgruppe Materialwissenschaft und Werkstofftechnik (520000)


Thematische Einordnung (Klassifikation)
DDC: 004

OpenAccess:
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Dokumenttyp
Journal Article

Format
online

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-105018184444
WOS Core Collection: WOS:001559184600001

Interne Identnummern
RWTH-2026-03846
Datensatz-ID: 1032869

Beteiligte Länder
Germany

Lizenzstatus der Zeitschrift

 GO

Medline ; Creative Commons Attribution CC BY 3.0 ; DOAJ ; OpenAccess ; Article Processing Charges ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Emerging Sources Citation Index ; Fees ; SCOPUS ; Web of Science Core Collection
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Faculty of Georesources and Materials Engineering (Fac.5) > Division of Materials Science and Engineering
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526810
520000
 Record created 2026-04-02, last modified 2026-04-03
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